Using Genome-Scale Metabolic Models to Compare Serovars of the Foodborne Pathogen Listeria monocytogenes

Abstract

Listeria monocytogenes is a microorganism of great concern for the food industry, most notably because it is the 2nd most deadly bacterial foodborne pathogen. Therefore, it is important to study the organism in order to identify novel methods of control. Systems biology is one such approach. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including L. monocytogenes. The objective of the work presented here was to generate GEMs for six different strains of L. monocytogenes, and to both qualitatively and quantitatively validate these GEMs with experimental data. Qualitative validation by comparison to phenotypic microarray data resulted in GEMs with nutrient utilization agreement similar to that of previously published GEMs. Additionally, aerobic batch growth experiments resulted in predictions for growth rate and growth yield that were strongly and significantly correlated with experimental values. These findings are significant because they show that these GEMs for L. monocytogenes are comparable in agreement between in silico predictions and in vitro results to published models of other organisms. Therefore, as with the other models, namely those for Escherichia coli, Staphylococcus aureus, Vibrio vulnificus, and Salmonella spp., they can be used to determine new methods of growth control and disease treatment. Additionally, the findings confirm the acceptability of using semi-automated tools, like those provided by KBase, to generate GEMs.